Overview
- Authors:
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Calyampudi Radhakrishna Rao
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Department of Statistics, The Pennsylvania State University, University Park, USA
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Helge Toutenburg
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Institut für Statistik, Universität München, München, Germany
- This book is an essential text for graduate statsitics courses and courses where linear models play a part.
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Table of contents (10 chapters)
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- Calyampudi Radhakrishna Rao, Helge Toutenburg
Pages 1-2
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- Calyampudi Radhakrishna Rao, Helge Toutenburg
Pages 3-18
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- Calyampudi Radhakrishna Rao, Helge Toutenburg
Pages 19-87
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- Calyampudi Radhakrishna Rao, Helge Toutenburg
Pages 89-110
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- Calyampudi Radhakrishna Rao, Helge Toutenburg
Pages 111-154
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- Calyampudi Radhakrishna Rao, Helge Toutenburg
Pages 155-176
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- Calyampudi Radhakrishna Rao, Helge Toutenburg
Pages 177-202
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- Calyampudi Radhakrishna Rao, Helge Toutenburg
Pages 203-227
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- Calyampudi Radhakrishna Rao, Helge Toutenburg
Pages 229-245
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- Calyampudi Radhakrishna Rao, Helge Toutenburg
Pages 247-284
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Back Matter
Pages 285-353
About this book
The book is based on both authors' several years of experience in teaching linear models at various levels. It gives an up-to-date account of the theory and applications of linear models. The book can be used as a text for courses in statistics at the graduate level and as an accompanying text for courses in other areas. Some of the highlights in this book are as follows. A relatively extensive chapter on matrix theory (Appendix A) provides the necessary tools for proving theorems discussed in the text and offers a selection of classical and modern algebraic results that are useful in research work in econometrics, engineering, and optimization theory. The matrix theory of the last ten years has produced a series of fundamental results about the definiteness of matrices, especially for the differences of matrices, which enable superiority comparisons of two biased estimates to be made for the first time. We have attempted to provide a unified theory of inference from linear models with minimal assumptions. Besides the usual least-squares theory, alternative methods of estimation and testing based on convex loss func tions and general estimating equations are discussed. Special emphasis is given to sensitivity analysis and model selection. A special chapter is devoted to the analysis of categorical data based on logit, loglinear, and logistic regression models. The material covered, theoretical discussion, and its practical applica tions will be useful not only to students but also to researchers and con sultants in statistics.
Reviews
From a review:
L'ENSEIGNEMENT MATHEMATIQUE
"This book provides an up-to-date account of the theory and applications of linear models. It can be used as a text for courses instatistics at the graduate level as well as an accompanying text for other courses in which linear models play a part."
